MedDataX Logo

Artificial Intelligence in Diagnosing Dysphagia Patients

NCT ID: NCT05098808

Last Updated: 2021-10-28

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

449 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-09-01

Study Completion Date

2021-10-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

In this prospective study we extracted acoustic parameters using PRAAT from patient's attempt to phonate during the clinical evaluation using a digital smart device. From these parameters we attempted (1) to define which of the PRAAT acoustic features best help to discriminate patients with dysphagia (2) to develop algorithms using sophisticated ML techniques that best classify those i) with dysphagia and those ii ) at high risk of respiratory complications due to poor cough force.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

This study was prospective study, and patients who visited the department of rehabilitation medicine in a single university-affiliated tertiary hospital with dysphagic symptoms from September 2019 to March 2021 were included.Voice recording was performed at the enrollment with blinded assessment, where the participants first visited the rehabilitation department with chief complaints of dysphagia. The cough sounds were recorded with an iPad (Apple, Cupertino, CA, USA) through an embedded microphone.

From the acoustic files we extracted fourteen voice parameters that include the average value and standard deviation of the fundamental frequency (f0), harmonic-to-noise ratio (HNR), the jitter that refers to frequency instability, and the shimmer that represents the amplitude instability of the sound signal.

Machine learning algorithms and sophisticated deep neural network analysis will be performed.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Respiration Disorders Swallowing Disorder Phonation Disorder Stroke Aspiration Pneumonia Aspiration; Liquids

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Dysphagia mild

Able to start oral feeding after assessment

Acoustic features (from signals obtained during phonation)

Intervention Type OTHER

Acoustic features will be obtained via phonation files.

A voice recorder application provided by Apple was used, and the sampling frequency of the sound was 44,100 Hz. The digitized cough sound signals were band-pass-filtered between 20 to 16,000 Hz to use data from the whole frequency band gathered by the iPad. In each case, the smart device was positioned 20cm from the patient

Dysphagia severe

Non oral feeding and high risk of aspiration

Acoustic features (from signals obtained during phonation)

Intervention Type OTHER

Acoustic features will be obtained via phonation files.

A voice recorder application provided by Apple was used, and the sampling frequency of the sound was 44,100 Hz. The digitized cough sound signals were band-pass-filtered between 20 to 16,000 Hz to use data from the whole frequency band gathered by the iPad. In each case, the smart device was positioned 20cm from the patient

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Acoustic features (from signals obtained during phonation)

Acoustic features will be obtained via phonation files.

A voice recorder application provided by Apple was used, and the sampling frequency of the sound was 44,100 Hz. The digitized cough sound signals were band-pass-filtered between 20 to 16,000 Hz to use data from the whole frequency band gathered by the iPad. In each case, the smart device was positioned 20cm from the patient

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

1. Suspected swallowing disorder who were referred for swallowing assessment
2. Dysphagia attributable to brain lesion including stroke

Exclusion Criteria

1. Participants who were unable to perform phonation
2. Participants who had no VFSS or standardized swallowing assessment results
3. Participants with no spirometric measurements
Minimum Eligible Age

19 Years

Maximum Eligible Age

90 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

The Catholic University of Korea

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Sun Im

associate professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Sun Im, MD PhD

Role: PRINCIPAL_INVESTIGATOR

The Catholic University of Korea

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Department of Rehabilitation Medicine Bucheon St Mary's Hospital, Catholic University of Korea, College of Medicine

Bucheon-si, Kyounggido, South Korea

Site Status

Countries

Review the countries where the study has at least one active or historical site.

South Korea

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

HC19EESE0060

Identifier Type: -

Identifier Source: org_study_id